Media bias has been extensively studied by both social and computational
sciences. However, current work still has a large reliance on human input and
subjective assessment to label biases. This is especially true for cable news
research. To address these issues, we develop an unsupervised machine learning
method to characterize the bias of cable news programs without any human input.
This method relies on the analysis of what topics are mentioned through Named
Entity Recognition and how those topics are discussed through Stance Analysis
in order to cluster programs with similar biases together. Applying our method
to 2020 cable news transcripts, we find that program clusters are consistent
over time and roughly correspond to the cable news network of the program. This
method reveals the potential for future tools to objectively assess media bias
and characterize unfamiliar media environments.

通过对使用命名实体识别分析主题并通过立场分析讨论这些主题的方法，我们开发了一种无需任何人为输入来定义有线新闻节目偏见的无监督机器学习方法，应用于 2020 年的有线新闻转录中，发现节目的聚类随时间的变化而保持稳定且大致对应于节目所属的有线新闻网络，该方法揭示了未来客观评估媒体偏见和描绘陌生媒体环境的潜力。